Classification study of skin sensitizers based on support vector machine and linear discriminant analysis

Ren, Yaoyao; Liu, Huanxiang; Xue, Chao‐Bin; Yao, Xiaojun; Liu, Mancang; Fan, Botao

doi:10.1016/j.aca.2006.05.027

Cited by 40 publications

(31 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimum separation hyperplane (OSH) is the hyperplane with the maximum margin for a given finite set of learning patterns [11]. Linear SVM performs binary pattern classification by finding OSH function f (x) = w T x − b.…”

Section: Mapped Vectorsmentioning

confidence: 99%

“…SVM can also be used in classifying complicated models by employing nonlinear kernels. The role of kernel functions is to perform computations in the original input space rather than the high-dimensional (even infinite) feature space [11]. Because only the inner product is involved …”

Section: Species Identificationmentioning

confidence: 99%

“…. , x n which are mapped into a feature space with the nonlinear function φ(x), and the computed dot products [11].…”

Section: Mapped Vectorsmentioning

confidence: 99%

“…SVM is a supervised learning technique that can be applied to classification or regression [11]. SVM algorithms are based on the statistical learning theory and the VapnikChervonenkis (VC) dimension [12].…”

Section: Species Identificationmentioning

confidence: 99%

See 3 more Smart Citations

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

View full text Add to dashboard Cite

Recommended by Subhas MukhopadhyayFish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

show abstract

Section: Mapped Vectorsmentioning

confidence: 99%

Section: Species Identificationmentioning

confidence: 99%

“…. , x n which are mapped into a feature space with the nonlinear function φ(x), and the computed dot products [11].…”

Section: Mapped Vectorsmentioning

confidence: 99%

Section: Species Identificationmentioning

confidence: 99%

See 2 more Smart Citations

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

View full text Add to dashboard Cite

show abstract

“…Ren et al [69] used a Support Vector Machine (SVM) [70] to develop a non-linear binary classification model for skin sensitization for a diverse set of 131 organic compounds tested in the LLNA. Six descriptors were selected by stepwise forward discriminant analysis (LDA) from a starting set of 282 CODESSA molecular descriptors.…”

Section: Minireviewmentioning

confidence: 99%

A Minireview of Available Skin Sensitization (Q)SARs/Expert Systems

et al. 2008

View full text Add to dashboard Cite

This minireview describes the state-of-the-art of available (Q)SARs/expert systems for skin sensitization and evaluates their utility for potential regulatory use. It is one of a series of minireviews in this journal and is based on an in-depth review performed by the European Chemicals Bureau (ECB) of the European Commissions Joint Research Centre (JRC) in support of the development of technical guidance for the implementation of the Registration, Evaluation, and Authorization of CHemicals (REACH) legislation. Most existing models fall into one of two main categories: either they are local in nature, usually specific to a chemical class or reaction chemical mechanism or else they are global in form, derived empirically using statistical methods. Some global QSARs have been recently characterized in accordance with the OECD validation principles and shown to be of limited use for regulatory applications. These have been evaluated in more detail elsewhere. Expert systems capable of predicting skin sensitization are discussed briefly. The strong mechanistic understanding of skin sensitization has facilitated the development of relative alkylation index (RAI) models. The RAI approach, relying on reactivity and hydrophobicity as the key parameters, actually appears to be the most promising means of deriving robust and mechanistically interpretable models which can be applied in a regulatory context. These are now referred to as Quantitative Mechanistic Models (QMM). Whilst hydrophobicity is conveniently modelled using log P, reactivity is often not easily determined from chemical structure. Initiatives are in progress to generate the necessary reactivity data for reactions relevant to skin sensitization but more resources are still required. A strategy of how information from using the RAI approach can be used in the evaluation of skin sensitization potential is described. This is the subject of on-going and future work.

show abstract

Toward an Optimal Approach for Variable Selection in Counter‐Propagation Neural Networks: Modeling Protein‐Tyrosine Kinase Inhibitory of Flavanoids Using Substituent Electronic Descriptors

Hemmateenejad

Mehdipour

Deeb

et al. 2011

Molecular Informatics

View full text Add to dashboard Cite

Counter propagation neural network (CPNN) is one of the attractive tools of classification in QSAR studies. A major obstacle in classification by CPNN is finding the best subset of variables. In this study, the performance of some different feature selection algorithms including F score-based ranking, eigenvalue ranking of PCs obtained from data set, Non-Error-Rate (NER) ranking of both descriptors and PCs, and 3-way handling of data, Parallel Factor Analysis (PARAFAC), was evaluated in order to find the best classification model. The methods were applied for modeling protein-tyrosine kinase inhibitory of some flavonoid derivatives using substituent electronic descriptors (SED) as novel source of electronic descriptors. The results showed that the best performance was achieved by F-score ranking while the NER ranking of principal components (PCs) showed very fluctuate results and the worst performance was belonging to PARAFAC-CPNN. Furthermore, comparison of results of these nonlinear algorithms with linear discriminate analysis method revealed better predictions by the former.

show abstract

Classification study of skin sensitizers based on support vector machine and linear discriminant analysis

Cited by 40 publications

References 27 publications

Intelligent Fish Freshness Assessment

Intelligent Fish Freshness Assessment

A Minireview of Available Skin Sensitization (Q)SARs/Expert Systems

Toward an Optimal Approach for Variable Selection in Counter‐Propagation Neural Networks: Modeling Protein‐Tyrosine Kinase Inhibitory of Flavanoids Using Substituent Electronic Descriptors

Contact Info

Product

Resources

About